Measuring the missing: greater racial and ethnic disparities in COVID-19 burden after accounting for missing race/ethnicity data.

• Type of Material: Article
• Authors: Katie Labgold, Emory University; Sarah Hamid, Emory University; Sarita Shah, Emory University; Neel Gandhi, Emory University; Allison Chamberlain, Emory University; Fazle Khan, Fulton County Board of Health.; Shamimul Khan, Fulton County Board of Health.; Sasha Smith, Fulton County Board of Health.; Steve Williams, Fulton County Board of Health.; Timothy Lash, Emory University; Lindsay J. Collin, Emory University
• Language: English
• Date: 2020-10-02
• Publisher: National Institutes of Health
• Publication Version: Preprint (Prior to Peer Review)
• Copyright Statement: © 2020 the authors.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1101/2020.09.30.20203315
• Title of Journal or Parent Work: medRxiv
• Grant/Funding Information: This work was supported in part by the US National Institutes of Health F31CA239566 (PI L. J. Collin), R01LM013049 (PI T. L. Lash), and K24AI114444 (PI N. R. Gandhi).; It was also supported by a grant from the Robert W. Woodruff foundation (PI A. Chamberlain).; K. Labgold is supported in part by the Center for Reproductive Health Research in the Southeast (RISE) Doctoral Fellowship and an ARCS Foundation Award.
• Keywords: COVID-19; Bioinformatics; Racial disparities; Data Collection; Epidemiology; Ethnic disparities
• Research Categories: Health Sciences, Epidemiology; Sociology, Ethnic and Racial Studies; Health Sciences, Public Health; Biology, Biostatistics

Abstract

Black, Hispanic, and Indigenous persons in the United States have an increased risk of SARS-CoV-2 infection and death from COVID-19, due to persistent social inequities. The magnitude of the disparity is unclear, however, because race/ethnicity information is often missing in surveillance data. In this study, we quantified the burden of SARS-CoV-2 infection, hospitalization, and case fatality rates in an urban county by racial/ethnic group using combined race/ethnicity imputation and quantitative bias-adjustment for misclassification. After bias-adjustment, the magnitude of the absolute racial/ethnic disparity, measured as the difference in infection rates between classified Black and Hispanic persons compared to classified White persons, increased 1.3-fold and 1.6-fold respectively. These results highlight that complete case analyses may underestimate absolute disparities in infection rates. Collecting race/ethnicity information at time of testing is optimal. However, when data are missing, combined imputation and bias-adjustment improves estimates of the racial/ethnic disparities in the COVID-19 burden.